The U.S. landscape has undergone substantial changes since Europeans first arrived. Many land use changes are attributable to human activity. Historical data concerning these changes are frequently limited and often difficult to develop. Modeling historical land use changes may be necessary. We develop annual population series from first European settlement to 1999 for all 50 states and Washington D.C. for use in modeling land use trends. Extensive research went into developing the historical data. Linear interpolation was used to complete the series after critically evaluating the appropriateness of linear interpolation versus exponential interpolation.

The world population surpassed eight billion people in 2022, having doubled from its figure less than 50 years previously. Looking forward, it is projected that the world population will reach nine billion in 2038, and 10 billion in 2060, but it will peak around 10.3 billion in the 2080s before it then goes into decline. Regional variations The global population has seen rapid growth since the early 1800s, due to advances in areas such as food production, healthcare, water safety, education, and infrastructure, however, these changes did not occur at a uniform time or pace across the world. Broadly speaking, the first regions to undergo their demographic transitions were Europe, North America, and Oceania, followed by Latin America and Asia (although Asia's development saw the greatest variation due to its size), while Africa was the last continent to undergo this transformation. Because of these differences, many so-called "advanced" countries are now experiencing population decline, particularly in Europe and East Asia, while the fastest population growth rates are found in Sub-Saharan Africa. In fact, the roughly two billion difference in population between now and the 2080s' peak will be found in Sub-Saharan Africa, which will rise from 1.2 billion to 3.2 billion in this time (although populations in other continents will also fluctuate). Changing projections The United Nations releases their World Population Prospects report every 1-2 years, and this is widely considered the foremost demographic dataset in the world. However, recent years have seen a notable decline in projections when the global population will peak, and at what number. Previous reports in the 2010s had suggested a peak of over 11 billion people, and that population growth would continue into the 2100s, however a sooner and shorter peak is now projected. Reasons for this include a more rapid population decline in East Asia and Europe, particularly China, as well as a prolongued development arc in Sub-Saharan Africa.

The data presented in this data project were collected in the context of two H2020 research projects: ‘Enhanced migration measures from a multidimensional perspective’(HumMingBird) and ‘Crises as opportunities: Towards a level telling field on migration and a new narrative of successful integration’(OPPORTUNITIES). The current survey was fielded to investigate the dynamic interplay between media representations of different migrant groups and the governmental and societal (re)actions to immigration. With these data, we provide more insight into these societal reactions by investigating attitudes rooted in values and worldviews. Through an online survey, we collected quantitative data on attitudes towards:

1. Immigrants, Refugees, Muslims, Hispanics, Venezuelans
2. News Media Consumption
3. Trust in News Media and Societal Institutions
4. Frequency and Valence of Intergroup Contact
5. Realistic and Symbolic Intergroup Threat
6. Right-wing Authoritarianism
7. Social Dominance Orientation
8. Political Efficacy
9. Personality Characteristics
10. Perceived COVID-threat, and
11. Socio-demographic Characteristics

1. Austria
2. Belgium
3. Germany
4. Hungary
5. Italy
6. Spain
7. Sweden

1. United States of America
2. Colombia

The survey in the United States and Colombia was identical to the one in the European countries, although a few extra questions regarding COVID-19 and some region-specific migrant groups (e.g. Venezuelans) were added. We collected the data in cooperation with Bilendi, a Belgian polling agency, and selected the methodology for its cost-effectiveness in cross-country research. Respondents received an e-mail asking them to participate in a survey without specifying the subject matter, which was essential to avoid priming. Three weeks of fieldwork in May and June of 2021 resulted in a dataset of 13,645 respondents (a little over 1500 per country). Sample weights are included in the dataset and can be applied to ensure that the sample is representative for gender and age in each country. The cooperation rate ranged between 12% and 31%, in line with similar online data collections.

The Country-Level Population and Downscaled Projections Based on Special Report on Emissions Scenarios (SRES) A1, B1, and A2 Scenarios, 1990-2100, were adopted in 2000 from population projections realized at the International Institute for Applied Systems Analysis (IIASA) in 1996. The Intergovernmental Panel on Climate Change (IPCC) SRES A1 and B1 scenarios both used the same IIASA "rapid" fertility transition projection, which assumes low fertility and low mortality rates. The SRES A2 scenario used a corresponding IIASA "slow" fertility transition projection (high fertility and high mortality rates). Both IIASA low and high projections are performed for 13 world regions including North Africa, Sub-Saharan Africa, China and Centrally Planned Asia, Pacific Asia, Pacific OECD, Central Asia, Middle East, South Asia, Eastern Europe, European part of the former Soviet Union, Western Europe, Latin America, and North America. This data set is produced and distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).

The Country-Level Population and Downscaled Projections Based on Special Report on Emissions Scenarios (SRES) B2 Scenario, 1990-2100, were based on the UN 1998 Medium Long Range Projection for the years 1995 to 2100. The official version projects population for 8 regions of the world including Africa, Asia (minus India and China), India, China, Europe, Latin America, Northern America, and Oceania. This data set is produced and distributed by the Columbia University Center for International Earth Science Information Network (CIESIN).

Number of households by type and number of components in the latest census in Milan and in 43 other European and US cities with a population of more than 700,000 inhabitants. The data has been harmonized from two international sources: a) Eurostat - Census hub 2011 b) US Census Bureau - American fact finder. For some cities, the data is provided in rounded form, for this reason the total of households may differ from the sum in terms of type and number.

Use this application to view the pattern of concentrations of people by race and Hispanic or Latino ethnicity. Data are provided at the U.S. Census block group level, one of the smallest Census geographies, to provide a detailed picture of these patterns. The data is sourced from the U.S Census Bureau, 2020 Census Redistricting Data (Public Law 94-171) Summary File. Definitions: Definitions of the Census Bureau’s categories are provided below. This interactive map shows patterns for all categories except American Indian or Alaska Native and Native Hawaiian or Other Pacific Islander. The total population countywide for these two categories is small (1,582 and 263 respectively). The Census Bureau uses the following race categories:Population by RaceWhite – A person having origins in any of the original peoples of Europe, the Middle East, or North Africa.Black or African American – A person having origins in any of the Black racial groups of Africa.American Indian or Alaska Native – A person having origins in any of the original peoples of North and South America (including Central America) and who maintains tribal affiliation or community attachment.Asian – A person having origins in any of the original peoples of the Far East, Southeast Asia, or the Indian subcontinent including, for example, Cambodia, China, India, Japan, Korea, Malaysia, Pakistan, the Philippine Islands, Thailand, and Vietnam.Native Hawaiian or Other Pacific Islander – A person having origins in any of the original peoples of Hawaii, Guam, Samoa, or other Pacific Islands.Some Other Race - this category is chosen by people who do not identify with any of the categories listed above. People can identify with more than one race. These people are included in the Two or More Races Hispanic or Latino PopulationThe Hispanic/Latino population is an ethnic group. Hispanic/Latino people may be of any race.Other layers provided in this tool included the Loudoun County Census block groups, towns and Dulles airport, and the Loudoun County 2021 aerial imagery.

This paper utilizes data on subjective probabilities to study the impact of the stock market crash of 2008 on households' expectations about the returns on the stock market index. We use data from the Health and Retirement Study that was fielded in February 2008 through February 2009. The effect of the crash is identified from the date of the interview, which is shown to be exogenous to previous stock market expectations. We estimate the effect of the crash on the population average of expected returns, the population average of the uncertainty about returns (subjective standard deviation), and the cross-sectional heterogeneity in expected returns (disagreement). We show estimates from simple reduced-form regressions on probability answers as well as from a more structural model that focuses on the parameters of interest and separates survey noise from relevant heterogeneity. We find a temporary increase in the population average of expectations and uncertainty right after the crash. The effect on cross-sectional heterogeneity is more significant and longer lasting, which implies substantial long-term increase in disagreement. The increase in disagreement is larger among the stockholders, the more informed, and those with higher cognitive capacity, and disagreement co-moves with trading volume and volatility in the market.

Number of population by gender and age recorded in the latest census in Milan and in 43 other European and US cities with a population of more than 700,000 inhabitants. The data has been harmonized from two international sources: * a) Eurostat - Census hub 2011 * b) US Census Bureau - American fact finder. For some cities the data is provided in rounded form, for this reason the total population may differ from the sum by gender and age.

The main objectives of this data collection effort were to assemble a set of cross-nationally comparable microdata samples for Economic Commission for Europe (ECE) countries based on the 1990 national population and housing censuses in countries of Europe and North America, and to use these samples to study the social and economic conditions of older persons. The samples are designed to allow research on a wide range of issues related to aging, as well as on other social phenomena. The Estonia microdata sample contains information on persons aged 50 and over and the persons who reside with them. Variables included in this dataset cover geographic area, type of residency, type of dwelling, and household characteristics, as well as demographic information such as age, sex, marital status, number of children, education, income, and occupation.

This dataset presents information on historical central government revenues for 31 countries in Europe and the Americas for the period from 1800 (or independence) to 2012. The countries included are: Argentina, Australia, Austria, Belgium, Bolivia, Brazil, Canada, Chile, Colombia, Denmark, Ecuador, Finland, France, Germany (West Germany between 1949 and 1990), Ireland, Italy, Japan, Mexico, New Zealand, Norway, Paraguay, Peru, Portugal, Spain, Sweden, Switzerland, the Netherlands, the United Kingdom, the United States, Uruguay, and Venezuela. In other words, the dataset includes all South American, North American, and Western European countries with a population of more than one million, plus Australia, New Zealand, Japan, and Mexico. The dataset contains information on the public finances of central governments. To make such information comparable cross-nationally we have chosen to normalize nominal revenue figures in two ways: (i) as a share of the total budget, and (ii) as a share of total gross domestic product. The total tax revenue of the central state is disaggregated guided by the Government Finance Statistics Manual 2001 of the International Monetary Fund (IMF) which provides a classification of types of revenue, and describes in detail the contents of each classification category. Given the paucity of detailed historical data and the needs of our project, we combined some subcategories. First, we are interested in total tax revenue (centaxtot), as well as the shares of total revenue coming from direct (centaxdirectsh) and indirect (centaxindirectsh) taxes. Further, we measure two sub-categories of direct taxation, namely taxes on property (centaxpropertysh) and income (centaxincomesh). For indirect taxes, we separate excises (centaxexcisesh), consumption (centaxconssh), and customs(centaxcustomssh).

For a more detailed description of the dataset and the coding process, see the codebook available in the .zip-file.

Purpose:

This dataset presents information on historical central government revenues for 31 countries in Europe and the Americas for the period from 1800 (or independence) to 2012. The countries included are: Argentina, Australia, Austria, Belgium, Bolivia, Brazil, Canada, Chile, Colombia, Denmark, Ecuador, Finland, France, Germany (West Germany between 1949 and 1990), Ireland, Italy, Japan, Mexico, New Zealand, Norway, Paraguay, Peru, Portugal, Spain, Sweden, Switzerland, the Netherlands, the United Kingdom, the United States, Uruguay, and Venezuela. In other words, the dataset includes all South American, North American, and Western European countries with a population of more than one million, plus Australia, New Zealand, Japan, and Mexico. The dataset contains information on the public finances of central governments. To make such information comparable cross-nationally we have chosen to normalize nominal revenue figures in two ways: (i) as a share of the total budget, and (ii) as a share of total gross domestic product. The total tax revenue of the central state is disaggregated guided by the Government Finance Statistics Manual 2001 of the International Monetary Fund (IMF) which provides a classification of types of revenue, and describes in detail the contents of each classification category. Given the paucity of detailed historical data and the needs of our project, we combined some subcategories. First, we are interested in total tax revenue (centaxtot), as well as the shares of total revenue coming from direct (centaxdirectsh) and indirect (centaxindirectsh) taxes. Further, we measure two sub-categories of direct taxation, namely taxes on property (centaxpropertysh) and income (centaxincomesh). For indirect taxes, we separate excises (centaxexcisesh), consumption (centaxconssh), and customs(centaxcustomssh).

This dataset contains information on:

· Estimated resident population (ERP) at 30 June 1996, 2001, 2006, 2013, and 2018 for total population

· ERP at 30 June 2018 by ethnic groups (European or Other (including New Zealander), Māori, Pacific, Asian, and Middle Eastern/Latin American/African) – estimates and percentage

· Sex ratio – number of males per 100 females

· ERP at 30 June 2018 by broad age groups and median age

· Geographies available are regional council areas, territorial authority and Auckland local board areas, Statistical Area 2, and urban rural.

Note: The geography corresponds to 2020 boundaries

Note: -999 indicates data are not available.

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About the estimated resident population

The estimated resident population at 30 June in the census year is based on the census usually resident population count, with updates for:

· net census undercount (as measured by a post-enumeration survey)

· residents temporarily overseas on census night

· births, deaths and net migration between census night and 30 June

· reconciliation with demographic estimates at the youngest ages.

The estimated resident population is not directly comparable with the census usually resident population count because of these adjustments.

For more detailed information about the methods used to calculate each base population, see DataInfo+ Demographic estimates.

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Ethnic groups

It is important to note that these ethnic groups are not mutually exclusive because people can and do identify with more than one ethnicity. People who identify with more than one ethnicity have been included in each ethnic group.

The 'Māori', 'Pacific', 'Asian' and 'Middle Eastern/Latin American/African' ethnic groups are defined in level 1 of the Ethnicity New Zealand Standard Classification 2005. The estimates for the 'European or Other (including New Zealander)' group include people who belong to the 'European' or 'Other ethnicity' groups defined in level 1 of the standard classification. If a person belongs to both the 'European' and 'Other ethnicity' groups they have only been counted once. Almost all people in the 'Other ethnicity' group belong to the 'New Zealander' sub-group.

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Time series

This time series is irregular. Because the 2011 Census was cancelled after the Canterbury earthquake on 22 February 2011, the gap between the 2006-base and 2013-base estimated resident population is seven years. The change in data between 2006 and 2013 may be greater than in the usual five-year gap between censuses. Be careful when comparing trends.

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Rounding

Individual figures may not sum to stated totals due to rounding.

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More information

See Estimated resident population (2018-base): At 30 June 2018 for commentary about the 2018 ERP.

Subnational population estimates concepts – DataInfo+ provides definitions of terms used in the map.

Access more population estimates data in NZ.Stat:

Theme: Population estimates.

The synthetic population was generated from the 2010-2014 ACS PUMS housing and person files. United States Department of Commerce. Bureau of the Census. (2017-03-06). American Community Survey 2010-2014 ACS 5-Year PUMS File [Data set]. Ann Arbor, MI: Inter-university Consortium of Political and Social Research [distributor]. http://doi.org/10.3886/E100486V1 Outputs There are 17 housing files - repHus0.csv, repHus1.csv, ... repHus16.csv and 32 person files - rep_recode_ACSpus0.csv, rep_recode_ACSpus1.csv, ... rep_recode_ACSpus31.csv. Files are split to be roughly equal in size. The files contain data for the entire country. Files are not split along any demographic characteristic. The person files and housing files must be concatenated to form a complete person file and a complete housing file, respectively. If desired, person and housing records should be merged on 'id'. Variable description is below. Data Dictionary See 2010-2014 ACS PUMS data dictionary. All variables from the ACS PUMS housing files are present in the synthetic housing files and all variables from the ACS PUMS person files are present in the synthetic person files. Variables have not been modified in any way. Theoretically, variables like person weight no longer have any use in the synthetic population. See README.md for more details. This work is supported under Grant G-2015-13903 from the Alfred P. Sloan Foundation on "The Economics of Socially-Efficient Privacy and Confidentiality Management for Statistical Agencies" (PI: John M. Abowd, https://www.ilr.cornell.edu/labor-dynamics-institute/research/project-19)

FUME data on projected distributions of migrants at local level between 2030 and 2050.

The dataset contains a folder of data for each destination city as a gridded dataset at 100m resolution in GeoTIFF format. The examined destination cities are: Amsterdam, Copenhagen, Krakow and Rome. The dataset is provided as 100m grid cells based on the Eurostat GISCO grid of the 2021 NUTS version, using ETRS89 Lambert Azimuthal Equal-Area (EPSG: 3035) as coordinate system. The file names consist of the projected year, the corresponding scenario, and the reference migrant group. The projections have been performed for the years 2030, 2040 and 2050. The investigated scenarios are the following: • benchmark (bs), • baseline (bs), • Rising East (re), • EU Recovery (eur), • Intensifying Global Competition (igc), and • War (war).

The migration background is derived from data about the Region of Origin (RoO) for migrants in Copenhagen and Amsterdam, and from Region of Citizenship (CoC) for migrants in Krakow and Rome.

The case study of Copenhagen covers the two central NUTS3 areas (DK011, DK012) and the groups presented are the following: • total population (totalpop), • native population (DNK), • Eastern EU European migrants (EU_East), • Western EU Europeans migrants (EU_West), • Non-EU European migrants (EurNonEU), • migrants from Turkey (Turkey), • the MENAP countries (MENAP; excluding Turkey), • other non-Western (OthNonWest), and • other Western countries (OthWestern).

The case study of Amsterdam covers one NUTS3 area (NL329) and the presented groups are the following: • total population (totalpop), • native population (NLD), • Eastern EU European migrants (EU East), • Western EU European migrants (EU West), • migrants from Turkey and Morocco (Turkey + Morocco), • migrants from the Middle East and Africa (Middle East + Africa), • migrants from the former colonies (Former Colonies), and • migrants from the rest of the world (Other Europe etc).

The case study of Krakow covers the Municipality of Krakow, and the presented groups are the following: • total population (totalpop), • native population (POL), • EU/EFTA European migrants (EU), • non-EU European migrants (Europe_nonEU), and • migrants from the rest of the world (Other).

The case of Rome covers the Municipality of Rome, and the presented groups are the following: • total population (totalpop), • native population (ITA), • migrants from Romania (ROU), • Philippines (PHL), • Bangladesh (BGD), • the EU (EU; excluding Romania), • Africa (Africa), • Asia (Asia; excluding Philippines and Bangladesh) and • America (America).

This database contains tobacco consumption data from 1970-2015 collected through a systematic search coupled with consultation with country and subject-matter experts. Data quality appraisal was conducted by at least two research team members in duplicate, with greater weight given to official government sources. All data was standardized into units of cigarettes consumed and a detailed accounting of data quality and sourcing was prepared. Data was found for 82 of 214 countries for which searches for national cigarette consumption data were conducted, representing over 95% of global cigarette consumption and 85% of the world’s population. Cigarette consumption fell in most countries over the past three decades but trends in country specific consumption were highly variable. For example, China consumed 2.5 million metric tonnes (MMT) of cigarettes in 2013, more than Russia (0.36 MMT), the United States (0.28 MMT), Indonesia (0.28 MMT), Japan (0.20 MMT), and the next 35 highest consuming countries combined. The US and Japan achieved reductions of more than 0.1 MMT from a decade earlier, whereas Russian consumption plateaued, and Chinese and Indonesian consumption increased by 0.75 MMT and 0.1 MMT, respectively. These data generally concord with modelled country level data from the Institute for Health Metrics and Evaluation and have the additional advantage of not smoothing year-over-year discontinuities that are necessary for robust quasi-experimental impact evaluations. Before this study, publicly available data on cigarette consumption have been limited—either inappropriate for quasi-experimental impact evaluations (modelled data), held privately by companies (proprietary data), or widely dispersed across many national statistical agencies and research organisations (disaggregated data). This new dataset confirms that cigarette consumption has decreased in most countries over the past three decades, but that secular country specific consumption trends are highly variable. The findings underscore the need for more robust processes in data reporting, ideally built into international legal instruments or other mandated processes. To monitor the impact of the WHO Framework Convention on Tobacco Control and other tobacco control interventions, data on national tobacco production, trade, and sales should be routinely collected and openly reported. The first use of this database for a quasi-experimental impact evaluation of the WHO Framework Convention on Tobacco Control is: Hoffman SJ, Poirier MJP, Katwyk SRV, Baral P, Sritharan L. Impact of the WHO Framework Convention on Tobacco Control on global cigarette consumption: quasi-experimental evaluations using interrupted time series analysis and in-sample forecast event modelling. BMJ. 2019 Jun 19;365:l2287. doi: https://doi.org/10.1136/bmj.l2287 Another use of this database was to systematically code and classify longitudinal cigarette consumption trajectories in European countries since 1970 in: Poirier MJ, Lin G, Watson LK, Hoffman SJ. Classifying European cigarette consumption trajectories from 1970 to 2015. Tobacco Control. 2022 Jan. DOI: 10.1136/tobaccocontrol-2021-056627. Statement of Contributions: Conceived the study: GEG, SJH Identified multi-country datasets: GEG, MP Extracted data from multi-country datasets: MP Quality assessment of data: MP, GEG Selection of data for final analysis: MP, GEG Data cleaning and management: MP, GL Internet searches: MP (English, French, Spanish, Portuguese), GEG (English, French), MYS (Chinese), SKA (Persian), SFK (Arabic); AG, EG, BL, MM, YM, NN, EN, HR, KV, CW, and JW (English), GL (English) Identification of key informants: GEG, GP Project Management: LS, JM, MP, SJH, GEG Contacts with Statistical Agencies: MP, GEG, MYS, SKA, SFK, GP, BL, MM, YM, NN, HR, KV, JW, GL Contacts with key informants: GEG, MP, GP, MYS, GP Funding: GEG, SJH SJH: Hoffman, SJ; JM: Mammone J; SRVK: Rogers Van Katwyk, S; LS: Sritharan, L; MT: Tran, M; SAK: Al-Khateeb, S; AG: Grjibovski, A.; EG: Gunn, E; SKA: Kamali-Anaraki, S; BL: Li, B; MM: Mahendren, M; YM: Mansoor, Y; NN: Natt, N; EN: Nwokoro, E; HR: Randhawa, H; MYS: Yunju Song, M; KV: Vercammen, K; CW: Wang, C; JW: Woo, J; MJPP: Poirier, MJP; GEG: Guindon, EG; GP: Paraje, G; GL Gigi Lin Key informants who provided data: Corne van Walbeek (South Africa, Jamaica) Frank Chaloupka (US) Ayda Yurekli (Turkey) Dardo Curti (Uruguay) Bungon Ritthiphakdee (Thailand) Jakub Lobaszewski (Poland) Guillermo Paraje (Chile, Argentina) Key informants who provided useful insights: Carlos Manuel Guerrero López (Mexico) Muhammad Jami Husain (Bangladesh) Nigar Nargis (Bangladesh) Rijo M John (India) Evan Blecher (Nigeria, Indonesia, Philippines, South Africa) Yagya Karki (Nepal) Anne CK Quah (Malaysia) Nery Suarez Lugo (Cuba) Agencies providing assistance: Iranian Tobacco Co. Institut National de la Statistique (Tunisia) HM Revenue & Customs (UK) Eidgenössisches Finanzdepartement EFD/Département...

The Small Area Estimates of Poverty and Inequality dataset consists of consumption-based

poverty, inequality and related measures for subnational administrative units in approximately twenty countries throughout Africa,

Asia, Europe, North America, and South America. These measures are derived on a country-level basis from a combination of census and

survey data using small area estimates techniques. The collection of data have been compiled, integrated and standardized from the

original data providers into a unified spatially referenced and globally consistent dataset. The data products include shapefiles

(vector data), tabular datasets (csv format), and centroids (csv file with latitude and longitude of a geographic unit and associated

poverty estimates). Additionally, a data catalog (xls format) containing detailed information and documentation is provided. This

dataset is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration

with a number of external data providers.

The genetic diversity of feral and ranch American mink was studied in order to detect gene flux among rivers, investigate the processes of invasion, and determine the possible effects of river barriers. Tissue samples of 78 feral American mink from 5 different river catchments and 18 ranch mink, collected between 2007 and 2011 in Biscay, northern Spain, were genotyped at 21 microsatellite loci. Lack of genetic differentiation of feral mink among the sites and high differentiation between feral and ranch mink was suggested. These results confirm that the mink population established on Butrón River at the beginning of the 1990s may be the origin of almost all the feral mink population within the study area. Additionally, the occurrence of American and European mink was used to analyse the effect of fragmentation on the population viability. The size and composition of the home range of male European mink was considered to model minimum viable units for presence/absence. Forty-two minimum viable units were randomly distributed among rivers in order to analyse the effect of fragmentation on mink occurrence. Barriers were mapped and classified as slight, moderate or absolute, depending on the effect on mink movement, and were introduced into the models. The presence of European and American mink depended on the non-fragmented main river stretches and the number of tributaries free from barriers. Results showed that fragmented rivers can be temporarily occupied but the likelihood of death means that these areas are only sink patches for mink.

The Caribbean basin is home to some of the most complex interactions in recent history among previously diverged human populations. Here, we investigate the population genetic history of this region by characterizing patterns of genome-wide variation among 330 individuals from three of the Greater Antilles (Cuba, Puerto Rico, Hispaniola), two mainland (Honduras, Colombia), and three Native South American (Yukpa, Bari, and Warao) populations. We combine these data with a unique database of genomic variation in over 3,000 individuals from diverse European, African, and Native American populations. We use local ancestry inference and tract length distributions to test different demographic scenarios for the pre- and post-colonial history of the region. We develop a novel ancestry-specific PCA (ASPCA) method to reconstruct the sub-continental origin of Native American, European, and African haplotypes from admixed genomes. We find that the most likely source of the indigenous ancestry in Caribbean islanders is a Native South American component shared among inland Amazonian tribes, Central America, and the Yucatan peninsula, suggesting extensive gene flow across the Caribbean in pre-Columbian times. We find evidence of two pulses of African migration. The first pulse—which today is reflected by shorter, older ancestry tracts—consists of a genetic component more similar to coastal West African regions involved in early stages of the trans-Atlantic slave trade. The second pulse—reflected by longer, younger tracts—is more similar to present-day West-Central African populations, supporting historical records of later transatlantic deportation. Surprisingly, we also identify a Latino-specific European component that has significantly diverged from its parental Iberian source populations, presumably as a result of small European founder population size. We demonstrate that the ancestral components in admixed genomes can be traced back to distinct sub-continental source populations with far greater resolution than previously thought, even when limited pre-Columbian Caribbean haplotypes have survived.

1. Although the phenology of numerous organisms has advanced significantly in response to recent climate change, the life history and population consequences of earlier reproduction remain poorly understood. 2. We analyzed extensive data on temporal change in laying date and clutch size of birds from Europe and North America to test whether these changes were related to recent trends in population size. 3. Across studies, laying date advanced significantly while clutch size did not change. However, within populations, change in laying date and change in clutch size were positively correlated, implying that species that advanced their laying date the most were also those that increased their clutch size the most. 4. Greater advances in laying date were associated with species that had multiple broods per season, lived in non-agricultural habitats and were herbivorous or predatory. The duration of the breeding season increased for multi-brooded species and decreased for single-brooded species. In contrast to studies of changes in spring arrival dates (for migratory species), the changes we found in laying date and clutch size were not related to changes in population size (for resident or migratory species). 5. This suggests that, across a wide variety of species, mismatches in the timing of egg-laying or numbers of offspring have had relatively little influence on population size compared with other aspects of phenology and life history.

Background: Invasive pest species have large impacts on agricultural crop yields, and understanding their population dynamics is important for ensuring food security. The oriental fruit moth Grapholita molesta is a cosmopolitan pest of stone and pome fruit species including peach and apple, and historical records indicate that it has invaded North and South America, Europe, Australia and Africa from its putative native range in Asia over the past century. Results: We used 13 microsatellite loci, including nine newly developed markers, to characterize global population structure of G. molesta. Approximately 15 individuals from each of 26 globally distributed populations were genotyped. A weak but significant global pattern of isolation-by-distance was found, and G. molesta populations were geographically structured on a continental scale. Evidence does not support that G. molesta was introduced to North America from Japan as previously proposed. However, G. molesta was probably introduced from North America to The Azores, South Africa, and Brazil, and from East Asia to Australia. Shared ancestry was inferred between populations from Western Europe and from Brazil, although it remains unresolved whether an introduction occurred from Europe to Brazil, or vice versa. Both genetic diversity and levels of inbreeding were surprisingly high across the range of G. molesta and were not higher or lower overall in introduced areas compared to native areas. There is little evidence for multiple introductions to each continent (except in the case of South America), or for admixture between populations from different origins. Conclusions: Cross-continental introductions of G. molesta appear to be infrequent, which is surprising given its rapid worldwide expansion over the past century. We suggest that area-wide spread via transport of fruits and other plant materials is a major mechanism of ongoing invasion, and management efforts should therefore target local and regional farming communities and distribution networks.